Most contemporary rounding mechanisms, whether implemented in hardware or software, arise in computational situations such as performance of mathematical (e.g., floating point) calculations, and therefore focus on mathematical accuracy in the result. Such rounding mechanisms typically introduce a round-off induced offset or bias in the result designed to ensure that “correct” or mathematically accurate results will be obtained in future calculations involving the rounded result.
In digital signal processing, however, mathematical accuracy is not necessarily the chief concern. For example, constellation diagrams (an two axis plot of the data states of phase or phase-amplitude encoded digital data) are commonly employed in wireless telecommunications systems utilizing, for instance, quad-state phase-shift keyed (QPSK) or quadrature amplitude modulation (QAM) signals.
In such circumstances, maintaining a mean value of the bipolar data stream as a whole (or of the constellation in the case of coded data) is of primary importance, while tolerance for round-off induced offset or bias is low. An unintentional distortion of the mean value could result in errors within the receiver due to an introduced offset in the processed, received data symbol constellation versus the symbol constellation diagram for a priori known and expected symbols.
There is, therefore, a need in the art for mean value preservation during round off of twos complement binary data.